In the technology of information visualization, traditional bar charts are sometimes used as a vehicle for conveying visual representations of information. For example, a bar chart may be used to represent sales volumes of a particular product over a one-year period. If each bar is used to represent sales within a particular month, the sales data is partitioned into twelve groups. The height of each month's bar may represent, for example, the total sales volume (e.g., the sum of all sales) for that month. Alternatively, the height of each month's bar may represent the average sale amount or the total number of units sold within the month, for example. Either way, the sales information within a month is “aggregated,” to define the height of the corresponding month's bar. No further information is typically provided, such as sales volume, sales distribution or exceptions, for example.
The above example of a bar chart indicates at least three limitations inherent in a traditional bar chart. First, by using data aggregation techniques, the values of the actual underlying data are obscured. Second, the data distributions are also obscured. Third, exceptional values (e.g., outliers) are not apparent from inspecting the bar chart.
Analysts often are faced with data analysis tasks that require detailed scrutiny of large volumes of multiple-attribute data. Because traditional bar charts are limited in the number of data attributes that they simultaneously can convey, information visualization technology developers continue to develop ways of displaying information in a manner in which large volumes of multiple data attributes may simultaneously be displayed and readily perceived.